20 citations · 22 across the 7 of their papers we have counts for
10 papers
Adapting model-based deep learning to multiple acquisition conditions: Ada-MoDL
Aniket Pramanik, Sampada Bhave, Saurav Sajib +2
Purpose: The aim of this work is to introduce a single model-based deep network that can provide high-quality reconstructions from undersampled parallel MRI data acquired with mult…
Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)
Aniket Pramanik, Mathews Jacob
Model-based deep learning methods that combine imaging physics with learned regularization priors have been emerging as powerful tools for parallel MRI acceleration. The main focus…
Memory-efficient model-based deep learning with convergence and robustness guarantees
Aniket Pramanik, M. Bridget Zimmerman, Mathews Jacob
Computational imaging has been revolutionized by compressed sensing algorithms, which offer guaranteed uniqueness, convergence, and stability properties. Model-based deep learning…
Improved Model based Deep Learning using Monotone Operator Learning (MOL)
Aniket Pramanik, Mathews Jacob
Model-based deep learning (MoDL) algorithms that rely on unrolling are emerging as powerful tools for image recovery. In this work, we introduce a novel monotone operator learning…
Joint Calibrationless Reconstruction and Segmentation of Parallel MRI
Aniket Pramanik, Xiaodong Wu, Mathews Jacob
The volume estimation of brain regions from MRI data is a key problem in many clinical applications, where the acquisition of data at high spatial resolution is desirable. While pa…
Reconstruction and Segmentation of Parallel MR Data using Image Domain DEEP-SLR
Aniket Pramanik, Mathews Jacob
The main focus of this work is a novel framework for the joint reconstruction and segmentation of parallel MRI (PMRI) brain data. We introduce an image domain deep network for cali…
ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms
Hemant Kumar Aggarwal, Aniket Pramanik, Maneesh John +1
Image reconstruction using deep learning algorithms offers improved reconstruction quality and lower reconstruction time than classical compressed sensing and model-based algorithm…
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)
Aniket Pramanik, Hemant Aggarwal, Mathews Jacob
Structured low-rank (SLR) algorithms, which exploit annihilation relations between the Fourier samples of a signal resulting from different properties, is a powerful image reconstr…
Calibrationless Parallel MRI using Model based Deep Learning (C-MODL)
Aniket Pramanik, Hemant Aggarwal, Mathews Jacob
We introduce a fast model based deep learning approach for calibrationless parallel MRI reconstruction. The proposed scheme is a non-linear generalization of structured low rank (S…
Off-the-grid model based deep learning (O-MODL)
Aniket Pramanik, Hemant Kumar Aggarwal, Mathews Jacob
We introduce a model based off-the-grid image reconstruction algorithm using deep learned priors. The main difference of the proposed scheme with current deep learning strategies i…